Maintenance cost estimation

ABSTRACT

Estimating maintenance for a storage system includes accessing a model that outputs time and materials estimates based on input configuration data, providing configuration data of the storage system to the model, and obtaining an estimate of maintenance time and materials based on the configuration data provided to the model. The model may be provided by a neural network, which may be a self-organized map. Weights of neurons of the self-organized map may be initialized randomly. The model may be initially configured using training data that may include an I/O load of the storage system, memory size of the storage system, a drive count of the storage system, and/or size and parameter information corresponding to hardware being added for the maintenance operation. The training data may include actual time and materials for prior storage system maintenance operations used for the training data. The model may be provided on the storage system.

TECHNICAL FIELD

This application relates to the field of computer systems and storagesystems therefor and, more particularly, to the field of estimatingmaintenance costs for storage systems.

BACKGROUND OF THE INVENTION

Host processor systems may store and retrieve data using a storagesystem containing a plurality of host interface units (I/O modules),disk drives, and disk interface units (disk adapters). The host systemsaccess the storage systems through a plurality of channels providedtherewith. Host systems provide data and access control informationthrough the channels to the storage system and the storage systemprovides data to the host systems also through the channels. The hostsystems do not address the disk drives of the storage system directly,but rather, access what appears to the host systems as a plurality oflogical disk units or logical devices. The logical devices may or maynot correspond to any one of the actual disk drives. Allowing multiplehost systems to access the single storage system allows the host systemsto share data stored therein among different host processor systems.

Many customers that use storage systems, such as banks, may require thatthe system be operational at all times. Achieving this entails employingredundant systems, having a failover strategy, etc. and also requires asignificant maintenance program to keep the hardware and softwareup-to-date and operating properly. Even in instances where continuousoperation is not necessarily required, proper maintenance and properoperation of a storage system may still be important. Depending in theamount and frequency of maintenance, the cost of maintenance for asystem could significantly exceed the initial cost of the hardware andsoftware for the system.

Generally, a vendor provides maintenance by initially estimating thecomponents and time needed (parts and labor) for a particularmaintenance operation. The customer is charged based on the estimate. Ifthe maintenance operation takes longer than expected and/or requiresmore or different components than originally estimated, then either thecustomer or the vendor must cover the additional, unexpected, cost. Ifthe vendor covers the additional cost, the vendor may lose moneyperforming the maintenance operation. The customer, on the other hand,may not want to pay any additional cost and, in some cases, may becontractually protected from maintenance cost overruns. Of course, thevendor may seek to prevent unexpected additional costs by providinghigher estimates for maintenance operations, but then the vendor maylose business to competitors that provide lower estimates. Moreover, thecustomer may not appreciate paying an amount for maintenance based on ahigher estimate for parts and labor that the customer does not receive.Thus, it is in the interest of the vendor to provide as accurate anestimate as possible. However, many storage systems are relativelycomplex and may be configured in a variety of different ways, thusmaking specific maintenance operations difficult to estimate; the costfor a particular maintenance operation on a one storage system may bevery different than the cost of the same maintenance operation on adifferent system due to the first storage system and the second storagesystem having very different configurations.

Accordingly, it is desirable to provide a mechanism that facilitatesaccurate estimates for maintenance of storage systems.

SUMMARY OF THE INVENTION

According to the system described herein, estimating maintenance for astorage system includes accessing a model that outputs time andmaterials estimates based on input configuration data, providingconfiguration data of the storage system to the model, and obtaining anestimate of maintenance time and materials based on the configurationdata provided to the model. The model may be provided by a neuralnetwork. The neural network may be a self-organized map. Weights ofneurons of the self-organized map may be initialized randomly. The modelmay be initially configured using training data. The training data mayinclude an I/O load of the storage system, memory size of the storagesystem, a drive count of the storage system, and/or size and parameterinformation corresponding to hardware being added for the maintenanceoperation. The size and parameter information corresponding to hardwarebeing added may include physical storage unit capacity of the hardware,a CPU count of the hardware, and/or a memory size of the hardware. Thetraining data may include actual time and materials for prior storagesystem maintenance operations used for the training data. The estimateof maintenance time and materials may be broken into separate phases.The model may be provided on the storage system.

According further to the system described herein, a non-transitorycomputer readable medium contains software that estimates maintenancefor a storage system. The software includes executable code thataccesses a model that outputs time and materials estimates based oninput configuration data, executable code that provides configurationdata of the storage system to the model, and executable code thatobtains an estimate of maintenance time and materials based on theconfiguration data provided to the model. The model may be provided by aneural network. The neural network may be a self-organized map. Weightsof neurons of the self-organized map may be initialized randomly. Themodel may be initially configured using training data. The training datamay include an I/O load of the storage system, memory size of thestorage system, a drive count of the storage system, and/or size andparameter information corresponding to hardware being added for themaintenance operation. The size and parameter information correspondingto hardware being added may include physical storage unit capacity ofthe hardware, a CPU count of the hardware, and/or a memory size of thehardware. The training data may include actual time and materials forprior storage system maintenance operations used for the training data.The estimate of maintenance time and materials may be broken intoseparate phases. The software may be provided on the storage system.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the system are described with reference to the severalfigures of the drawings, noted as follows.

FIG. 1 is a schematic illustration showing a relationship between a hostand a storage system that may be used in connection with an embodimentof the system described herein.

FIG. 2 is a schematic diagram illustrating an embodiment of a storagesystem where each of a plurality of directors are coupled to the memoryaccording to an embodiment of the system described herein.

FIG. 3 is a schematic illustration showing a storage area network (SAN)providing a SAN fabric coupling a plurality of host systems to aplurality of storage systems that may be used in connection with anembodiment of the system described herein.

FIG. 4 is a flow diagram illustrating processing performed in connectionwith constructing and using a model that estimates maintenance time andmaterials using a neural network.

FIG. 5 is a schematic illustration showing a self-organized map using inconnection with an embodiment of the system described herein.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

The system described herein uses a neural network to estimate time andmaterials for a prospective maintenance operation. The neural networkmay be a self-organized map that is trained using customerconfigurations and maintenance time and materials from prior maintenanceoperations.

FIG. 1 is a diagram 20 showing a relationship between a host 22 and astorage system 24 that may be used in connection with an embodiment ofthe system described herein. In an embodiment, the storage system 24 maybe a PowerMax, Symmetrix, or VMAX storage system produced by Dell EMC ofHopkinton, Mass.; however, the system described herein may operate withother appropriate types of storage systems. Also illustrated is another(remote) storage system 26 that may be similar to, or different from,the storage system 24 and may, in various embodiments, be coupled to thestorage system 24, using, for example, a network. The host 22 reads andwrites data from and to the storage system 24 via an HA 28 (hostadapter), which facilitates an interface between the host 22 and thestorage system 24. Although the diagram 20 shows the host 22 and the HA28, it will be appreciated by one of ordinary skill in the art thatmultiple host adaptors (possibly of different configurations) may beused and that one or more HAs may have one or more hosts coupledthereto.

In an embodiment of the system described herein, in various operationsand scenarios, data from the storage system 24 may be copied to theremote storage system 26 via a link 29. For example, transferring datamay be part of a data mirroring or replication process that causes dataon the remote storage system 26 to be identical to the data on thestorage system 24. Although only the one link 29 is shown, it ispossible to have additional links between the storage systems 24, 26 andto have links between one or both of the storage systems 24, 26 andother storage systems (not shown). The storage system 24 may include afirst plurality of remote adapter units (RA's) 30 a, 30 b, 30 c. TheRA's 30 a-30 c may be coupled to the link 29 and be similar to the HA28, but are used to transfer data between the storage systems 24, 26.

The storage system 24 may include one or more physical storage units(including disks, solid state storage devices, etc.), each containing adifferent portion of data stored on the storage system 24. FIG. 1 showsthe storage system 24 having a plurality of physical storage units 33a-33 c. The storage system 24 (and/or remote storage system 26) may beprovided as a stand-alone device coupled to the host 22 as shown in FIG.1 or, alternatively, the storage system 24 (and/or remote storage system26) may be part of a storage area network (SAN) that includes aplurality of other storage systems as well as routers, networkconnections, etc. (not shown in FIG. 1). The storage systems may becoupled to a SAN fabric and/or be part of a SAN fabric. The systemdescribed herein may be implemented using software, hardware, and/or acombination of software and hardware where software may be stored in acomputer readable medium and executed by one or more processors.

Each of the physical storage units 33 a-33 c may be coupled to acorresponding disk adapter unit (DA) 35 a-35 c that provides data to acorresponding one of the physical storage units 33 a-33 c and receivesdata from a corresponding one of the physical storage units 33 a-33 c.An internal data path exists between the DA's 35 a-35 c, the HA 28 andthe RA's 30 a-30 c of the storage system 24. Note that, in otherembodiments, it is possible for more than one physical storage unit tobe serviced by a DA and that it is possible for more than one DA toservice a physical storage unit. The storage system 24 may also includea global memory 37 that may be used to facilitate data transferredbetween the DA's 35 a-35 c, the HA 28 and the RA's 30 a-30 c as well asfacilitate other operations. The memory 37 may contain task indicatorsthat indicate tasks to be performed by one or more of the DA's 35 a-35c, the HA 28 and/or the RA's 30 a-30 c, and may contain a cache for datafetched from one or more of the physical storage units 33 a-33 c.

The storage space in the storage system 24 that corresponds to thephysical storage units 33 a-33 c may be subdivided into a plurality ofvolumes or logical devices. The logical devices may or may notcorrespond to the storage space of the physical storage units 33 a-33 c.Thus, for example, the physical storage unit 33 a may contain aplurality of logical devices or, alternatively, a single logical devicecould span both of the physical storage units 33 a, 33 b. Similarly, thestorage space for the remote storage system 26 may be subdivided into aplurality of volumes or logical devices, where each of the logicaldevices may or may not correspond to one or more physical storage unitsof the remote storage system 26.

In some embodiments, another host 22′ may be provided. The other host22′ is coupled to the remote storage system 26 and may be used fordisaster recovery so that, upon failure at a site containing the host 22and the storage system 24, operation may resume at a remote sitecontaining the remote storage system 26 and the other host 22′. In somecases, the host 22 may be directly coupled to the remote storage system26, thus protecting from failure of the storage system 24 withoutnecessarily protecting from failure of the host 22.

FIG. 2 is a schematic diagram 40 illustrating an embodiment of thestorage system 24 where each of a plurality of directors 42 a-42 n arecoupled to the memory 37. Each of the directors 42 a-42 n represents atleast one of the HA 28, RAs 30 a-30 c, or DAs 35 a-35 c. The diagram 40also shows an optional communication module (CM) 44 that provides analternative communication path between the directors 42 a-42 n. Each ofthe directors 42 a-42 n may be coupled to the CM 44 so that any one ofthe directors 42 a-42 n may send a message and/or data to any other oneof the directors 42 a-42 n without needing to go through the memory 37.The CM 44 may be implemented using conventional MUX/router technologywhere one of the directors 42 a-42 n that is sending data provides anappropriate address to cause a message and/or data to be received by anintended one of the directors 42 a-42 n that is receiving the data. Someor all of the functionality of the CM 44 may be implemented using one ormore of the directors 42 a-42 n so that, for example, the directors 42a-42 n may be interconnected directly with the interconnectionfunctionality being provided on each of the directors 42 a-42 n. Inaddition, one or more of the directors 42 a-42 n may be able tobroadcast a message to all or at least some plurality of the otherdirectors 42 a-42 n at the same time.

In some embodiments, one or more of the directors 42 a-42 n may havemultiple processor systems thereon and thus may be able to performfunctions for multiple discrete directors. In some embodiments, at leastone of the directors 42 a-42 n having multiple processor systems thereonmay simultaneously perform the functions of at least two different typesof directors (e.g., an HA and a DA). Furthermore, in some embodiments,at least one of the directors 42 a-42 n having multiple processorsystems thereon may simultaneously perform the functions of at least onetype of director and perform other processing with the other processingsystem. In addition, all or at least part of the global memory 37 may beprovided on one or more of the directors 42 a-42 n and shared with otherones of the directors 42 a-42 n. In an embodiment, the featuresdiscussed in connection with the storage system 24 may be provided asone or more director boards having CPUs, memory (e.g., DRAM, etc.) andinterfaces with Input/Output (I/O) modules.

Note that, although specific storage system configurations are disclosedin connection with FIG. 1 and FIG. 2, it should be understood that thesystem described herein may be implemented on any appropriate platform.Thus, the system described herein may be implemented using a platformlike that described in connection with FIGS. 1 and 2 or may beimplemented using a platform that is somewhat or even completelydifferent from any particular platform described herein.

A storage area network (SAN) may be used to couple one or more hostsystems with one or more storage systems in a manner that allowsreconfiguring connections without having to physically disconnect andreconnect cables from and to ports of the devices. A storage areanetwork may be implemented using one or more switches to which thestorage systems and the host systems are coupled. The switches may beprogrammed to allow connections between specific ports of devicescoupled to the switches. A port that can initiate a data-path connectionmay be called an “initiator” port while the other port may be deemed a“target” port.

FIG. 3 is a schematic illustration 70 showing a storage area network(SAN) 60 providing a SAN fabric coupling a plurality of host systems(H₁-H_(N)) 22 a-c to a plurality of storage systems (SD₁-SD_(N)) 24 a-cthat may be used in connection with an embodiment of the systemdescribed herein. Each of the devices 22 a-c, 24 a-c may have acorresponding port that is physically coupled to switches of the SANfabric used to implement the storage area network 60. The switches maybe separately programmed by one of the devices 22 a-c, 24 a-c or by adifferent device (not shown). Programming the switches may includesetting up specific zones that describe allowable data-path connections(which ports may form a data-path connection) and possible allowableinitiator ports of those configurations. For example, there may be azone for connecting the port of the host 22 a with the port of thestorage system 24 a. Upon becoming activated (e.g., powering up), thehost 22 a and the storage system 24 a may send appropriate signals tothe switch(es) of the storage area network 60, and each other, whichthen allows the host 22 a to initiate a data-path connection between theport of the host 22 a and the port of the storage system 24 a. Zones maybe defined in terms of a unique identifier associated with each of theports, such as such as a world-wide port name (WWPN).

In various embodiments, the system described herein may be used inconnection with performance data collection for data migration and/ordata mirroring techniques using a SAN. Data transfer among storagesystems, including transfers for data migration and/or mirroringfunctions, may involve various data synchronization processing andtechniques to provide reliable protection copies of data among a sourcesite and a destination site. In synchronous transfers, data may betransmitted to a remote site and an acknowledgement of a successfulwrite is transmitted synchronously with the completion thereof. Inasynchronous transfers, a data transfer process may be initiated and adata write may be acknowledged before the data is actually transferredto directors at the remote site. Asynchronous transfers may occur inconnection with sites located geographically distant from each other.Asynchronous distances may be distances in which asynchronous transfersare used because synchronous transfers would take more time than ispreferable or desired. Examples of data migration and mirroring productsincludes Symmetrix Remote Data Facility (SRDF) products from Dell EMC.

Referring to FIG. 4, a flow diagram 100 illustrates processing performedin connection with constructing and using a model that estimatesmaintenance time and materials for storage systems using a neuralnetwork that has been trained with empirical data from prior maintenanceoperations. Processing begins at a first step 102 where hardware andsoftware configuration information from prior maintenance operations aswell as maintenance parameters from the prior maintenance operations(e.g., actual time and materials for the prior maintenance e operations)is provided to the model. The hardware and software configurationinformation may include any parameters that may be potentially relevantand may potentially impact the time and materials needed for maintenanceoperations, including local and global memory amounts, current usage ofthe storage system, internal communication loads, existing I/O loads,physical components of the storage system and interconnections thereof,etc. Note that, prior to constructing/training a model, it may not bepossible to determine, with certainty, which configuration parameters orcombinations of parameters affect maintenance time and materials for aparticular maintenance operation. However, it is possible for engineersand maintenance personal that are familiar with storage systemmaintenance operations to choose configuration parameters that areexpected to be significant. Moreover, the neural network and the modelconstructing mechanism are expected to effectively determine an amountof impact (weights) for configuration parameters and combinationsthereof, as explained in more detail elsewhere herein.

Following the step 102 is a step 104 where the information/data providedat the step 102 is used to create a model that may be used to estimatemaintenance time and materials for prospective maintenance operations.In an embodiment herein, the model is constructed using aSelf-Organizing Map (SOM) algorithm—which is a type of neural networkthat is capable of discovering hidden non-linear structure in highdimensional data. For the SOM neural network used herein, the weights ofthe neurons are initialized to small random values as a first step toconstructing the model. In other embodiments, the weights may beinitialized based on expected final values for the weights as a way tohave the SOM model converge in less iterations. After initializing theweights, the model may be provided with the information/data from thestep 102, which uses each set of data to first determine a Euclideandistance to all weight vectors. A neuron having a weight vector that ismost similar to the input is deemed to be the best matching unit (BMU).The weights of the BMU and neurons close to the BMU in the SOM grid areadjusted towards the input vector. The magnitude of the change decreaseswith time and with the grid-distance from the BMU. This may be repeatedfor all of the sets of input data and/or until the SOM model converges.Note that other types of machine learning unsupervised models may beused instead of the SOM model illustrated herein.

The SOM algorithm and training of SOM networks is generally known in theart. The system described herein uses the different input variables(dimensions) associated with different maintenance operations along withknown associated resulting time and materials maintenance values totrain the SOM model used for predicting future maintenance time andmaterials. In an embodiment herein, a separate model is constructed foreach type of possible maintenance operation that may be performed on astorage system. For example, a particular model may correspond to anonline engine add maintenance procedure (i.e., adding a physical storageunit like the physical storage units 33 a-33 c, discussed above) inwhich the storage system is up and running while more storage capacityis added to the storage system. To train a SOM model, training data isconstructed by identifying a set of “features/dimensions” which mayinclude storage system I/O load, memory size, drive count, etc. of thestorage system, the size and parameters of the hardware being added(e.g., physical storage unit capacity, CPU count, memory size etc.), andthe actual time and materials needed for the online engine addmaintenance operation. After training with several examples of differentmaintenance procedures, the model is ready to estimate time andmaterials for a prospective maintenance operation in response to beingprovided appropriate input parameters. Following the step 104 is a teststep 106 where the system essentially polls to wait for a user torequest an estimate of a maintenance operation. After the model isconstructed at the steps 102, 104, the system waits for a request for anestimate. If it is determined at the test step 106 that an estimate hasbeen requested, control passes from the test step 106 to a step 108where data is provided for the estimate. The data includes some of thedimensions that were input for training and the model output is aprediction of time and material estimate. Following the step 108 is astep 112 where the model estimates the time and materials for themaintenance operation based on the inputs provided at the step 108. Inan embodiment herein, the time and materials estimate provided at thestep 112 may be broken down into different phases of the maintenanceoperation such as setup time, time for a first phase, time for a secondphase, etc. For example, for an online engine add maintenance operation,the result from the step 112 may provide separate times for estimatedsetup time, a first amount of estimated time to add the engine (physicalstorage unit), hook at cables and bring the new engine online, a secondamount of estimated time to redistribute global memory of the storagesystem to include the new engine memory portion, a third amount ofestimated time to re-distribute disk data of the storage system toinclude the new disks in the new engine, and an estimated clean up time.Note that providing separate estimates for different phases of amaintenance operation allows providing higher service level objectivesfor some phases and lower service level objectives for other phases.

Following the step 112 is a step 114 where the maintenance operation isperformed. Following the step 114 is a step 116 where the actualmaintenance operation time and the materials used for the maintenanceoperation, along with all of the other parameters (e.g., the parametersprovided at the step 108), are processed as an additional set oftraining data to the model. In an embodiment herein, after the model iscreated at the step 104, additional training data may be provided toimprove the performance of the model. Processing additional trainingdata at the step 116 is similar to the processing at the step 104,described above. Following the step 116, control transfers back to thestep 106, described above, to wait for a next request for an estimate.In some embodiments, data from actual maintenance operations is not usedto improve/train the model. This is represented by an alternative path118, which shows that after the model generates an estimate at the step112, control transfers back to the step 106 to wait for a next requestfor another estimate. Note that the processing illustrated by the flowdiagram 100 could be performed either by a storage system (like thestorage system 24 or the storage system 26) or could be performed by aseparate computing device, such as a laptop or desktop computer or evena smartphone or a tablet.

Referring to FIG. 5, a diagram 130 illustrates in more detail aself-organized map (SOM) neural network. Construction and use of a SOMis known in the art. A SOM is a discrete, planar grid of neurons havinga hidden layer and fed by an input layer. Each hidden layer neuron hasseveral neighbor neurons. A distance between each neuron and itsneighbors is a Euclidean distance between the input-to-hidden layerweights of each neuron. The distance encodes low-dimensional informationabout the original data. Each hidden layer neuron in the diagram 130 hasa weight vector with n components; the input is densely connected to thehidden layer.

Baseline training data is fed to SOM model. As mentioned above, eachcomponent of the training data is deemed a dimension, so that sampledata x_(i) is a sample vector of m dimensions x_(i)=d₁, d₂, d₃, . . . ,d_(m). The SOM model reduces the m input dimensions to a lesser numberof output dimensions (e.g., one or two dimensions). Thus, for example,even though there may be m input dimensions for the maintenanceoperation of replacing an online engine, the result of the SOM is justthe estimated maintenance time and estimated cost for materials. It isknown in the art that SOM neural networks by nature are effective infinding relationships between input data (configuration parameters, inthe system described herein) and using the input data to predict (orclassify) new data (maintenance operation estimates, in the systemdescribed herein). It is possible to extract more complex patterns byfeeding more input dimensions to the SOM model. For instance, in thesystem described herein where maintenance operation estimates areprovided as output, the additional inputs could include additionalcustomer environment information and known I/O performance profile. TheSOM model tries to fit the input data and predict the amount of time andmaterials (parts) needed for maintenance procedures. The SOM model usesan unsupervised iterative training procedure to analyze large amounts ofdata. In the system described herein, the SOM model is used to clustervarious daily maintenance procedures from several customers and severaldata sets into a manageable number of groupings. The SOM model producesan organized, low-dimensional array of patterns that represent a rangeof conditions found in the input data.

Various embodiments discussed herein may be combined with each other inappropriate combinations in connection with the system described herein.Additionally, in some instances, the order of steps in the flowdiagrams, flowcharts and/or described flow processing may be modified,where appropriate. Furthermore, various aspects of the system describedherein may be implemented using software, hardware, a combination ofsoftware and hardware and/or other computer-implemented modules ordevices having the described features and performing the describedfunctions. The system may further include a display and/or othercomputer components for providing a suitable interface with a userand/or with other computers.

Software implementations of the system described herein may includeexecutable code that is stored in a non-transitory computer-readablemedium and executed by one or more processors. The computer-readablemedium may include volatile memory and/or non-volatile memory, and mayinclude, for example, a computer hard drive, ROM, RAM, flash memory,portable computer storage media such as a CD-ROM, a DVD-ROM, an SD card,a flash drive or other drive with, for example, a universal serial bus(USB) interface, and/or any other appropriate tangible or non-transitorycomputer-readable medium or computer memory on which executable code maybe stored and executed by a processor. The system described herein maybe used in connection with any appropriate operating system.

Other embodiments of the invention will be apparent to those skilled inthe art from a consideration of the specification or practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with the true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A method of providing maintenance to a storagesystem, comprising: initializing weights of neurons of a plurality ofself-organized map neural network models that output time and materialsestimates based on input configuration data, wherein a separate model isconstructed for each type of maintenance operation that may be performedon the storage system; providing training data that is used to adjustthe weights of each of neurons of each of the self-organized map neuralnetwork models; providing configuration data of the storage system tothe one of the self-organized map neural network models corresponding tothe type of maintenance operation being performed; obtaining an estimateof maintenance time and materials needed for the maintenance operationbased on the configuration data provided to the one of the modelscorresponding to the type of maintenance operation being performed;performing the maintenance; and providing actual maintenance parametersresulting from performing the maintenance as additional training datafor the one of the models corresponding to the type of maintenanceoperation being performed.
 2. A method, according to claim 1, whereinweights of neurons of the self-organized map are initialized randomly.3. A method, according to claim 1, wherein the training data includes atleast one of: an I/O load of the storage system, memory size of thestorage system, a drive count of the storage system, and size andparameter information corresponding to hardware being added for themaintenance operation.
 4. A method, according to claim 3, wherein thesize and parameter information corresponding to hardware being addedincludes at least one of: physical storage unit capacity of thehardware, a CPU count of the hardware, and a memory size of thehardware.
 5. A method, according to claim 1, wherein the training dataincludes actual time and materials for prior storage system maintenanceoperations used for the training data.
 6. A method, according to claim1, wherein the estimate of maintenance time and materials is broken intoseparate phases.
 7. A method, according to claim 1, wherein the model isprovided on the storage system.
 8. A method, according to claim 1,wherein at least one of the models corresponds to an online engine addmaintenance procedure in which the storage system is running while morestorage capacity is added to the storage system.
 9. A non-transitorycomputer readable medium containing software that facilitates providingmaintenance to a storage system, the software comprising: executablecode that initializes weights of neurons of a plurality ofself-organized map neural network models that output time and materialsestimates based on input configuration data, wherein a separate model isconstructed for each type of maintenance operation that may be performedon the storage system and wherein training data that is used to adjustthe weights of each of neurons of each of the self-organized map neuralnetwork models; executable code that provides configuration data of thestorage system to the one of the self-organized map neural networkmodels corresponding to the type of maintenance operation beingperformed; executable code that obtains an estimate of maintenance timeand materials needed for the maintenance operation based on theconfiguration data provided to the one of the self-organized map neuralnetwork models corresponding to the type of maintenance operation beingperformed; and executable code that provides actual maintenanceparameters resulting from performing the maintenance as additionaltraining data for the one of the models corresponding to the type ofmaintenance operation being performed.
 10. A non-transitory computerreadable medium, according to claim 9, wherein weights of neurons of theself-organized map are initialized randomly.
 11. A non-transitorycomputer readable medium, according to claim 9, wherein the trainingdata includes at least one of: an I/O load of the storage system, memorysize of the storage system, a drive count of the storage system, andsize and parameter information corresponding to hardware being added forthe maintenance operation.
 12. A non-transitory computer readablemedium, according to claim 11, wherein the size and parameterinformation corresponding to hardware being added includes at least oneof: physical storage unit capacity of the hardware, a CPU count of thehardware, and a memory size of the hardware.
 13. A non-transitorycomputer readable medium, according to claim 9, wherein the trainingdata includes actual time and materials for prior storage systemmaintenance operations used for the training data.
 14. A non-transitorycomputer readable medium, according to claim 9, wherein the estimate ofmaintenance time and materials is broken into separate phases.
 15. Anon-transitory computer readable medium, according to claim 9, whereinthe software is provided on the storage system.
 16. A non-transitorycomputer readable medium, according to claim 9, wherein at least one ofthe models corresponds to an online engine add maintenance procedure inwhich the storage system is running while more storage capacity is addedto the storage system.